Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem in wireless sensor networks (WSNs) and a new simple but effective decision fusion rule based on belief function theory is proposed. Unlike exi...

Five methods for count data clusterization based on Poisson mixture models are described. Two of them are parametric, the others are semi-parametric. The methods emlploy the plug-in Bayes classification rule. Their performance is investigated by making use of computer simulation and compared mainly by the clusterization error rate. We also apply the clusterization procedures to real count data ...

We propose a modal logic based on three operators, representing intial beliefs, information and revised beliefs. Three simple axioms are used to provide a sound and complete axiomatization of the qualitative part of Bayes’ rule. Some theorems of this logic are derived concerning the interaction between current beliefs and future beliefs. Information flows and iterated revision are also discussed.

We justify generalisations of weak values from a tentatively relational perspective by deriving them from a generalisation of Bayes’ rule. We also argue that these generalisations have implications of quantum nonlocality and may form a novel approach to quantum gravity and cosmology.

  Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...

BACKGROUND Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated classification of records into syndromes--syndromic surveillance--using algorithms that incorporate medical knowledge in ...

Since, by (8) pertaining to the nearest neighbor decision rule (NN rule). We briefly review the NN rule and then describe the CNN rule. The NN rule['l-[ " I assigns an unclassified sample to the same class as the nearest of n stored, correctly classified samples. In other words, given a collection of n reference points, each classified by some external source, a new point is assigned to the sam...

This document discusses Bayesian classification in the context of supervised learning. Supervised learning is defined. An approach is described in which feature likelihooods are estimated from data, and then classification is done by computing class posteriors given features using Bayes rule. Estimating of feature likelihoods, independence of features, quantization of features, and information ...